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Article Development of Flood Damage Regression Models by Rainfall Identification Reflecting Landscape Features in Gangwon Province, the Republic of Korea
Hyun Il Choi
Department of Civil Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan 38541, Gyeongbuk, Korea; [email protected]; Tel.: +82-53-810-2413
Abstract: Torrential rainfall events associated with rainstorms and typhoons are the main causes of flood-related economic losses in Gangwon Province, Republic of Korea. The frequency and severity of flood damage have been increasing due to frequent extreme rainfall events as a result of climate change. Rainfall is a major cause of flood damage for the study site, given a strong relationship between the probability of flood damage over the last two decades and the maximum rainfall for 6 and 24 h durations in the 18 administrative districts of Gangwon Province. This study aims to develop flood damage regression models by rainfall identification for use in a simplified and efficient assessment of flood damage risk in ungauged or poorly gauged regions. Optimal simple regression models were selected from four types of non-linear functions with one of five composite predictors averaged for the two rainfall datasets. To identify appropriate predictor rainfall variables indicative of regional landscape features, the relationships between the composite rainfall predictor and landscape characteristics such as district size, topographic features, and urbanization rate were interpreted. The proposed optimal regression models may provide governments and policymakers with an efficient flood damage risk map simply using a regression outcome to design or forecast rainfall data.
Citation: Choi, H.I. Development of Keywords: flood damage; rainfall; landscape; simple regression; damage risk map Flood Damage Regression Models by Rainfall Identification Reflecting Landscape Features in Gangwon Province, the Republic of Korea. Land 1. Introduction 2021 10 , , 123. https://doi.org/ Global warming and climate change have increased the frequency and severity of 10.3390/land10020123 extreme weather events, which has in turn elevated the risk of severe climate-related natural disasters [1–3]. Natural disasters may directly incur substantial human and economic Received: 20 December 2020 damage costs, and flood-related disasters are one of the most frequent and deadliest Accepted: 26 January 2021 Published: 27 January 2021 natural disasters worldwide [4]. The Korean Peninsula annually experiences flood damage by the East Asian monsoon, and the flood damage costs caused by rainstorms and typhoons
Publisher’s Note: MDPI stays neutral account for the majority of damage losses caused by natural disasters in the Republic of with regard to jurisdictional claims in Korea [5]. Climate changes may also have a greater influence on extreme rainfall patterns published maps and institutional affil- in Gangwon Province than in other regions of the Korean Peninsula. This is related to the iations. complex geographical landscape of the province associated with the Taebaek Mountain Range and the East Sea. These features divide the province into the western region with a mountainous climate and the eastern region with an oceanic climate. In terms of the historic extreme events, Gangneung City in the eastern province received the highest recorded daily rainfall of 880 mm. This was considered a 200-year event, due to a localized downpour Copyright: © 2021 by the author. Licensee MDPI, Basel, Switzerland. from severe thunderstorms by Typhoon Rusa on 31 August 2002 [5,6]. On 29 August 2018, This article is an open access article Cheorwon County in the western province recorded the heaviest downpour, measuring distributed under the terms and 113.5 mm/h with a return period exceeding 500 years, due to a localized stagnant front conditions of the Creative Commons created between the cold air mass from the northwest and the hot and humid air mass Attribution (CC BY) license (https:// from the East Sea [5]. A number of severe localized downpours associated with torrential creativecommons.org/licenses/by/ rainstorms and super typhoons frequently occur because of the mountainous and coastal 4.0/). landscapes characteristic to Gangwon Province. The major countermeasures against the
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flood damage have focused on supporting recovery costs for flood damaged areas in the Republic of Korea [5]. As such, preemptive flood management measures are required to reduce the human and economic damage costs from recent flood disasters. Assessment of the vulnerability or risk to regional flood hazard is one of the non-structural measures to prepare integrated mitigation measures customized to regional flood damage [7,8]. For proactive approaches to flood risk management strategies, there is a need for a method that can predict future flood damage risk by analyzing the characteristics and trends of regional flood damage records [9]. Flood damage risk or vulnerability assessments should be based on flood hazard and inundation analysis results using hydrologic and hydraulic models. However, the lack of available hydrological data and information of a decent quality introduces a degree of uncertainty in validating model simulation results, particularly the case for ungauged regions. The lack of reliable data is a crucial barrier to flood damage analysis and flood risk assessment [10]. To resolve these issues, regression analysis presents itself as an alter- native method that may be an effective tool in predicting hydrological variables through acceptable relationships with influencers to overcome limited hydrological data in spatial and temporal resolutions on target regions to be analyzed [11]. Many studies have shown that rainfall characteristics have a significant impact on flood damage events from complex influencing factors [12–19]. Elucidation of a functional relationship between rainfall and flood damage could relate the amount of flood damage or flood events to rainfall conditions. As such, the risk of flood damage may also be estimated by determining the rainfall–flood damage relationship through regression analysis [12,15,17,19]. Most previous studies have conducted regression analysis using the fixed predictor rainfall variables in a single regres- sion function to develop regional damage regression models. However, the variations in flood damage attributable to rainfall were not high in some rainfall-flood damage regres- sion models. To improve the prediction performance of rainfall-flood damage regression analysis, it is necessary to identify rainfall variables that reflect regional characteristics; these typically have a non-linear relationship with the features of flood damage. The aim of this study is to provide a methodology to develop rainfall-flood damage regression models for assessing the relative flood damage risk associated with a specific amount of rainfall for designing or forecasting purposes. This paper proposes optimal regression models to estimate regional flood damage. These models were selected from four types of regression functions, with one of the five predictor rainfall variables capable of representing the regional landscape and terrain features. The proposed methodology was implemented through various regression analysis models for Gangwon Province, Republic of Korea. This area characterized by a complex landscape of mountainous and coastal areas and lacks in available and/or reliable hydrological data. Flood damage data caused by rainstorms and typhoons were collected from annual disaster reports [5], provided by the Ministry of the Interior and Safety for the last 20 years from 1999 to 2018. The analysis period over the last two decades was determined by comprehensively considering the amount of data necessary for regression analysis and the consistency in damage features of past data for the study area. Rainfall data were collected from 16 automated surface observing system (ASOS) meteorological stations [20], managed by the Korea Meteorological Agency around the 18 administrative districts of Gangwon Province. The ASOS meteorological gauge stations undertake continuous measurements of hourly rainfall observations for the analysis period of flood damage records. Although there are no generalized guidelines for sample size requirements appropriate for regression analysis, this study has adopted one of the various rules-of-thumb that recommends at least 10 cases per variable [21–23]. Therefore, several non-linear functions were applied to a simple regression analysis with a single composite predictor averaged by different rainfall characteristics. This accounted for the minimum number of 12 damage records for the study site. The identification of a suitable predictor rainfall variable that incorporates regional landscape features may improve the possible applications of rainfall–flood damage regression results. Land 2021, 10, 123 3 of 14
2. Materials and Methods 2.1. Study Region Figure1 shows that the Gangwon Province is located between 37 ◦020 N–38◦370 N and 127◦050 E–129◦220 E in the mid-eastern part of the Korean Peninsula. It is located at the eastern end of the Asian continent bordered by the East Sea, a margin of the Western Pacific Ocean. The Gangwon Province comprises 18 administrative districts (7 cities and 11 counties), spanning an area of 16,874 km2; this makes up 16.8% of the national territory of the Republic of Korea. Figure2a illustrates that the landscape is dominated by the Taebaek Mountains, which divides the province into two parts—the eastern region has a relatively steep coastline facing the East Sea, whereas the western region is most pronounced in complex mountain terrains containing the headwaters of major rivers in the Republic of Korea, including the Han and Nakdong Rivers. Gangwon Province is generally a mountainous region with a lowland area of less than 100 EL.m, occupying only 5.6% of the total area of the province. The urbanization rate in the province is much lower than the national average due to this rugged mountainous landscape. Figure2b shows that the urban areas and towns are predominantly located along the coastline in the eastern region. These include (2) Sokcho City, (4) Gangneung City, (5) Donghae City, (6) Samcheok City, and (7) Taebaek City, which are scattered in the Taebaek Mountains. Only two cities are located in the lowland areas of the western region, namely (12) Chuncheon City, the capital, and (16) Wonju City. The climate conditions differ considerably between the eastern and western regions, as the regions are geographically divided by the Taebaek Mountains. The oceanic climate features predominate in the eastern region with steep mountain slopes descending to the coastline. In contrast, the western region predominantly exhibits continental climate features and some highlands around the Taebaek Mountains experience mountain climate characteristics. The climate is characterized by high temperature and humidity due to the temperate climate in summer and low temperature and humidity in winter due to the high continental pressure. The annual average precipitation of Gangwon Province is 1358.9 mm for the last two decades (1999–2018). Approximately 65% of annual precipitation is concentrated in the summer season from June to September [20]. This is mainly due to the East Asian summer monsoon rainfall and the number of typhoon events affecting the Korean Peninsula almost every year. This monsoonal rainfall has caused severe annual flood damage Land 2021, 10, x FOR PEER REVIEW events in nearly all 18 administrative districts of the province (see also Table1 and Figure4 of 315 for details).
FigureFigure 1. Study 1. Study area area (Gangwon (Gangwon Province, Province, Korea) Korea) and and the thelocation location of the of the18 administ 18 administrativerative districts districts in the in theprovince. province.
(a) (b) Figure 2. Landscape of the 18 administrative districts of Gangwon Province: (a) terrain landscape; (b) urban area. Number in parentheses indicates the identification number of each administrative district.
2.2. Data The National Disaster Information Center [5] provides the annual flood damage rec- ords of rainstorms and typhoons, along with information on the date and place of each damage event. Flood damage records were collected over the last two decades from 1999 to 2018 for 7 cities and 11 counties in Gangwon Province. This vast amount of data was obtained to secure an adequate amount of historical data necessary for consistency in damage features and also to fulfill the requirements in the rainfall–flood damage regres- sion analysis. Table 1 summarizes the data statistics of economic damage records caused by rainstorms and typhoons from 1999 to 2018 for the 18 administrative districts. There was an annual average of 1.2 flood damage events (434 flood damage events over 20 years per 18 administrative districts) for each administrative district over the last two decades. In terms of the total occurrence number of economic flood damage events, rainstorm-in- duced damage events were approximately twice the typhoon-induced events. However, typhoons have incurred much larger cumulative and district average damage costs than rainstorms. The difference is even greater in terms of damage intensity (economic losses per damage event); typhoon-induced damage intensity was approximately three times rainstorm-induced damage intensity for each administrative district. Figure 3 also indi- cates that these economic damage events caused by typhoons were more frequent than or comparable to those caused by rainstorms in the eastern districts. In contrast, the western Land 2021, 10, x FOR PEER REVIEW 4 of 15
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Figure 1. Study area (Gangwon Province, Korea) and the location of the 18 administrative districts in the province.
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districts experienced more frequent economic damage events caused by rainstorms than by typhoons over the last two decades. These distinct regional flood damage patterns are mainly due to the regional complex landscape associated with the Taebaek Mountains and the East Sea. Note that all typhoon-caused damage data were included in the analysis (a) (b) as there are rainfall records for each typhoon-induced damage event in Gangwon Prov- Figure 2. Landscape of the 18ince administrative over the last districts two decades. of Gangwon Province: (a) terrain landscape; (b) urban area. Number Figure 2. Landscape of the 18 administrative districts of Gangwon Province: (a) terrain landscape; (b) urban area. Number in parentheses indicates the identification number of each administrative district. in parentheses indicates the identification number of each administrative district. Table 1. Statistics on the economic costs of damage events caused by rainstorms and typhoons in 2.2.the 18Data administrative districts of Gangwon Province from 1999 to 2018. Table 1. Statistics on the economic costs of damage events caused by rainstorms and typhoons in the The National Disaster Information Center [5] provides the annual flood damage rec- 18 administrativeGross districts Total of Gangwon 1 Number Province of from Annual 1999 to 2018. Average 3 Damage Intensity 3 ordsCauses of rainstorms and typhoons, along with information on the date and place of each (US$) Cases 2 (US$/year) (US$/event) damage event. FloodGross damage Total 1recordsNumber were ofcollectedAnnual over Average the last3 twoDamage decades Intensity from 19993 RainstormsCauses 2,088,396,922 280 2 5,801,103 414,364 to 2018 for 7 cities and(US$) 11 counties inCases Gangwon Province.(US$/Year) This vast amount(US$/Event) of data was Typhoons 3,406,875,471 154 9463.543 1,229,032 obtainedRainstorms to secure 2,088,396,922an adequate amount 280 of historical 5,801,103 data necessary for consistency 414,364 in 1 Total economic losses3,406,875,471 to assets including 154buildings, ships, 9463.543public facilities, crops, 1,229,032etc., based on damageTyphoons features and also to fulfill the requirements in the rainfall–flood damage regres- 1the consumer price index and US $ rate in 2018 over the 18 administrative districts. 2 Sum of the sionTotal analysis. economic Table lossesto 1 assetssummarizes including the buildings, data ships,statistics public of facilities, economic crops, damage etc., based records on the consumercaused pricetotal indexdamage and events US $ rate over in 2018the 18 over administrative the 18 administrative districts. districts. 3 Average2 Sum damage of the total costs damage per administra- events over the by18tive administrativerainstorms districts. districts.and typhoons3 Average from damage 1999 costs to per 2018 administrative for the 18 districts. administrative districts. There was an annual average of 1.2 flood damage events (434 flood damage events over 20 years 40 per 18 administrative districts) for each administrative district over the last two decades. In termsRainstorm-caused of the total occurrence Damage numberTyphoon-caused of economic flood Damage damage events, rainstorm-in- 30 duced damage events were4 approximately twice the typhoon-induced11 events. However, 8 66 14 8 6 20 11 typhoons12 have incurred much 4larger cumulative and district average damage10 costs than 13 6 7 11 rainstorms.12 The difference25 is even greater in terms of damage intensity (economic losses 10 5 18 21 21 21 18 21 20 14 per damage14 event);16 typhoon-induced damage intensity was approximately three15 times 10 13 13 7 rainstorm-induced6 damage7 intensity for each administrative district. Figure 3 also indi- Damage Events Damage 0 cates that these economic damage events caused by typhoons were more frequent than or comparable to those caused by rainstorms in the eastern districts. In contrast, the western
Eastern Districts Western Districts
Figure 3.3. Number of economic damage records ofof rainstormsrainstorms andand typhoonstyphoons forfor eacheach administrativeadministrative district in GangwonGangwon Province from 19991999 toto 2018.2018.
2.2. DataFor rainfall–flood damage regression analysis, rainfall observations were also col- lected from 16 ASOS meteorological stations under the Korea Meteorological Agency [20]. The National Disaster Information Center [5] provides the annual flood damage These rainfall data spatially affect the 18 administrative districts; the 16 AOGS stations records of rainstorms and typhoons, along with information on the date and place of each have been able to continuously secure hourly rainfall data without missing data for the damage event. Flood damage records were collected over the last two decades from 1999 last two decades. Rainfall data considered for this study included those for the analysis to 2018 for 7 cities and 11 counties in Gangwon Province. This vast amount of data was period from one day prior to the start date to the end date of each flood damage event for obtained to secure an adequate amount of historical data necessary for consistency in the 18 administrative districts to accommodate for the influential rainfall characteristics damage features and also to fulfill the requirements in the rainfall–flood damage regression that generate each flood damage event. Figure 4 shows that the areal average rainfall was analysis. Table1 summarizes the data statistics of economic damage records caused by computed based on these rainfall data from the 16 ASOS stations using the Thiessen pol- rainstorms and typhoons from 1999 to 2018 for the 18 administrative districts. There was ygon method [24], which is a spatial interpolation technique commonly used in engineer- an annual average of 1.2 flood damage events (434 flood damage events over 20 years ing hydrology. Thiessen polygons are generated from the bisector lines of two neighbor- per 18 administrative districts) for each administrative district over the last two decades. ing stations, and each polygon that contains a station represents the rainfall for that sta- tion. The areal average rainfall was interpolated using the ratios of the Thiessen polygons within a district. Note that ASOS stations are sparsely distributed in the northern districts bordered by the military demarcation line. The rainfall characteristics for regression anal- ysis were tentatively selected as the maximum rainfall recorded during damage events of 1, 2, 3, 6, 12, and 24 h (𝑅 , 𝑅 , 𝑅 , 𝑅 , 𝑅 , and 𝑅 , respectively). These durations repre- sent standard durations typically used for the purposes of designing, planning, forecast- ing, or warning. Land 2021, 10, 123 5 of 14
In terms of the total occurrence number of economic flood damage events, rainstorm- induced damage events were approximately twice the typhoon-induced events. However, typhoons have incurred much larger cumulative and district average damage costs than rainstorms. The difference is even greater in terms of damage intensity (economic losses per damage event); typhoon-induced damage intensity was approximately three times rainstorm-induced damage intensity for each administrative district. Figure3 also indicates that these economic damage events caused by typhoons were more frequent than or comparable to those caused by rainstorms in the eastern districts. In contrast, the western districts experienced more frequent economic damage events caused by rainstorms than by typhoons over the last two decades. These distinct regional flood damage patterns are mainly due to the regional complex landscape associated with the Taebaek Mountains and the East Sea. Note that all typhoon-caused damage data were included in the analysis as there are rainfall records for each typhoon-induced damage event in Gangwon Province over the last two decades. For rainfall–flood damage regression analysis, rainfall observations were also col- lected from 16 ASOS meteorological stations under the Korea Meteorological Agency [20]. These rainfall data spatially affect the 18 administrative districts; the 16 AOGS stations have been able to continuously secure hourly rainfall data without missing data for the last two decades. Rainfall data considered for this study included those for the analysis period from one day prior to the start date to the end date of each flood damage event for the 18 administrative districts to accommodate for the influential rainfall characteristics that generate each flood damage event. Figure4 shows that the areal average rainfall was computed based on these rainfall data from the 16 ASOS stations using the Thiessen poly- gon method [24], which is a spatial interpolation technique commonly used in engineering hydrology. Thiessen polygons are generated from the bisector lines of two neighboring stations, and each polygon that contains a station represents the rainfall for that station. The areal average rainfall was interpolated using the ratios of the Thiessen polygons within a district. Note that ASOS stations are sparsely distributed in the northern districts bor- dered by the military demarcation line. The rainfall characteristics for regression analysis were tentatively selected as the maximum rainfall recorded during damage events of 1, 2, 3, 6, 12, and 24 h (R1, R2, R3, R6, R12, and R24, respectively). These durations represent standard durations typically used for the purposes of designing, planning, forecasting, Land 2021, 10, x FOR PEER REVIEW 6 of 15 or warning.
FigureFigure 4.4. Thiessen polygons polygons overlain overlain with with the the 16 16 au automatedtomated surface surface observing observing system system (ASOS) (ASOS) meteorologicalmeteorological stations stations spatially spatially reflecting reflecting the the 18 18 administrative administrative districts districts of of Gangwon Gangwon Province. Province.
2.3. Relation Functions The proposed damage regression models were intended to estimate the relative flood damage risk for a specific amount of rainfall for making design decisions or forecasting, as opposed to predicting the precise cost of flood damage. Previous studies have demon- strated that a significant relationship exists between rainfall characteristics and flood dam- age features; hence, rainfall data can be used for flood damage risk assessments by utiliz- ing regression functions to estimate the probability of occurrence of flood damage events with respect to a specific amount of rainfall recorded [12,13,15,18]. As the relationship between rainfall and flood damage is also dependent on regional characteristics such as landscape and climate, various regression functions need to be considered while selecting the optimum goodness-of-fit among them, pertaining to each administrative district. Hence, this study used four types of regression functions based on rational functions in Equations (1) and (2) and logistic functions in Equations (3) and (4), as shown below. 𝐷 = 𝛼 𝛽𝐼 (1) 1 𝐷
𝐷 = 𝛼 𝛽𝑙𝑛𝐼 (2) 1 𝐷 𝐷 𝑙𝑛 = 𝛼 𝛽𝐼 (3) 1 𝐷
𝐷 𝑙𝑛 = 𝛼 𝛽𝑙𝑛𝐼 (4) 1 𝐷 where the dependent (response) variable 𝐷 is the probability of a flood damage event; the independent (predictor) variable 𝐼 is the rainfall amount averaged by a weighting fac- tor; 𝑤 (see Equation (5) for details); and 𝛼 and 𝛽 are regression coefficients. Flood dam- age records were converted into flood damage density (economic damage costs per dis- trict area), which represents the areal density of property damaged by a flood event. A log-normal distribution function was used to compute the occurrence probability of flood damage density; this is generally considered suitable for flood damage data [25]. To identify a single predictor variable, 𝐼 , in the regression functions in Equations (1)–(4), rainfall characteristics that were highly correlated with flood damage features over the 18 administrative districts were selected. Generally, the 6 h maximum rainfall 𝑅 and the 24 h maximum rainfall 𝑅 had higher Pearson correlation coefficients than the other Land 2021, 10, 123 6 of 14
2.3. Relation Functions The proposed damage regression models were intended to estimate the relative flood damage risk for a specific amount of rainfall for making design decisions or forecast- ing, as opposed to predicting the precise cost of flood damage. Previous studies have demonstrated that a significant relationship exists between rainfall characteristics and flood damage features; hence, rainfall data can be used for flood damage risk assessments by utilizing regression functions to estimate the probability of occurrence of flood damage events with respect to a specific amount of rainfall recorded [12,13,15,18]. As the relation- ship between rainfall and flood damage is also dependent on regional characteristics such as landscape and climate, various regression functions need to be considered while select- ing the optimum goodness-of-fit among them, pertaining to each administrative district. Hence, this study used four types of regression functions based on rational functions in Equations (1) and (2) and logistic functions in Equations (3) and (4), as shown below.
D = α + βI (1) 1 − D w D = α + βlnI (2) 1 − D w D ln = α + βI (3) 1 − D w D ln = α + βlnI (4) 1 − D w where the dependent (response) variable D is the probability of a flood damage event; the independent (predictor) variable Iw is the rainfall amount averaged by a weighting factor; w (see Equation (5) for details); and α and β are regression coefficients. Flood damage records were converted into flood damage density (economic damage costs per district area), which represents the areal density of property damaged by a flood event. A log-normal distribution function was used to compute the occurrence probability of flood damage density; this is generally considered suitable for flood damage data [25]. To identify a single predictor variable, Iw, in the regression functions in Equations (1)–(4), rainfall characteristics that were highly correlated with flood damage features over the 18 administrative districts were selected. Generally, the 6 h maximum rainfall R6 and the 24 h maximum rainfall R24 had higher Pearson correlation coefficients than the other rainfall characteristics over the 18 administrative districts. This outcome was based on the correlation between each of the six rainfall values (R1, R2, R3, R6, R12, and R24) and the probability of flood damage records for each administrative district (see Figure5). Accordingly, the two rainfall features R6 for a short duration and R24 for a long duration were selected. To incorporate the effect of the two characteristics of R6 and R24 into a single predictor variable, a composite rainfall variable Iw was proposed as a weighted geometric mean of the two rainfall factors:
w (1−w) Iw = R6 ·R24 (5)
where a weighting factor w representing the relative effect ratio of R6 to R24 was assumed for the five cases; these were 0, 0.25, 0.5, 0.75, and 1. Note that Io or I1 indicates that only R24 or R6 represent the regional rainfall characteristics, respectively. Therefore, each administrative district may have a total of 20 regression models using the four types of regression functions in Equations (1)–(4), with each of the five predictor variables such as I0 (R24), I0.25, I0.5, I0.75, and I1 (R6) in Equation (5). Then, the optimal regression model was selected for each administrative district to compare the significance and variation explained by the 20 regression functions. For robust regression analysis, any outliers in the original dataset were excluded once they were detected by the three Land 2021, 10, x FOR PEER REVIEW 7 of 15
rainfall characteristics over the 18 administrative districts. This outcome was based on the correlation between each of the six rainfall values (𝑅 , 𝑅 , 𝑅 , 𝑅 , 𝑅 , and 𝑅 ) and the probability of flood damage records for each administrative district (see Figure 5). Accordingly, the two rainfall features 𝑅 for a short duration and 𝑅 for a long du- ration were selected. To incorporate the effect of the two characteristics of 𝑅 and 𝑅 into a single predictor variable, a composite rainfall variable 𝐼 was proposed as a weighted geometric mean of the two rainfall factors: